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Microsoft MLOps Engineer Associate training (AI-300) - operationalise machine learning and generative AI on Azure. The new exam that replaces DP-100.
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The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate credential (exam AI-300) is a new 2026 certification that replaces DP-100 (Azure Data Scientist Associate). It validates the ability to operationalise machine learning and generative AI solutions on Azure - taking systems that have been built and running them reliably in production. This course prepares you for the exam: designing and implementing an MLOps infrastructure; implementing and managing the machine learning model lifecycle and operations; implementing a GenAIOps infrastructure; managing generative AI quality assurance and observability; and optimising generative AI solutions - using Azure Machine Learning, Microsoft Foundry, MLflow, GitHub Actions and infrastructure-as-code. The shift from DP-100 matters: DP-100 focused on building and training models, while AI-300 focuses on operating ML and generative AI systems in production (MLOps and the newer GenAIOps). There is no formal prerequisite for AI-300. Microsoft recommends hands-on experience with Azure Machine Learning, Python and MLOps - but you do not need to hold another certification first.
Organisations no longer just build models - they run machine learning and generative AI in production, at scale, reliably. That is MLOps (and, for generative AI, GenAIOps), and AI-300 is Microsoft's new credential for it. It proves you can deploy, monitor, evaluate and optimise ML and generative AI systems on Azure. Because it replaces DP-100 and reflects where the market is heading, it is a timely, in-demand certification - and the "MLOps Engineer" role is generally valued more highly than the older "Data Scientist" title.
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AI-300 is for people who put machine learning and generative AI into production: - AI and machine learning engineers moving models into production - MLOps and platform engineers supporting ML and generative AI - Data scientists adding production and operations skills - Azure practitioners working with ML and generative AI There is no formal prerequisite, but Microsoft recommends hands-on experience with Azure Machine Learning, Python and MLOps.
How to earn it:
1. Build hands-on experience with Azure Machine Learning, MLOps pipelines and generative AI on Azure (Microsoft Foundry, MLflow, GitHub Actions, infrastructure-as-code).
2. Practise the full operations lifecycle: deploying models, monitoring, drift detection, re...
The Microsoft MLOps Engineer Associate (AI-300) certification exam evaluates your ability to operationalize machine learning solutions and manage the lifecycle of AI and ML workloads on Microsoft Azure. It focuses on machine learning operations, model deployment, automation, monitoring, infrastructure, CI/CD workflows, and responsible management of ML systems. Understanding the exam structure, key technical domains, and certification requirements can help candidates prepare effectively and demonstrate their ability to build reliable, scalable, and production-ready machine learning solutions.
Certification details verified on 25 August 2026. AI-300 is a new 2026 certification that replaces DP-100 (retired 1 June 2026) and has no required prerequisite. The exam is about 100 minutes of exam time within a roughly 120-minute seat via Pearson VUE, with a passing score of 700 on a 100-1000 scale and a fee of US $165 (varies by country). The certification is valid for 12 months and renews for free online through Microsoft Learn. As a new exam, confirm the current objectives, format, passing score and fee on the Microsoft site.
This certification is for people who operationalise machine learning and generative AI: - AI and machine learning engineers moving models into production - MLOps and platform engineers supporting ML and generative AI - Data scientists adding production and operations skills - Azure practitioners working with ML and generative AI There is no required prerequisite, though Microsoft recommends hands-on experience with Azure Machine Learning, Python and MLOps. Our course builds the skills with hands-on MLOps and GenAIOps labs on Azure, and includes practice questions and mock exams to get you ready to pass.
